Time series spectral imaging facilitates a comprehensive understanding of the underlying dynamics of multi-component systems and processes. Most existing classification strategies focus exclusively on the spectral features and they tend to fail when spectra between classes closely resemble each other. This work proposes a hybrid approach of principal component analysis (PCA) and deep learning (i.e., long short-term memory (LSTM) model) for incorporating and utilizing the combined multi-temporal and spectral information from time series spectral imaging datasets. An example data, consisting of times series spectral images of casein-based biopolymers, was used to illustrate and evaluate the proposed hybrid approach. Compared to using partial least squares discriminant analysis (PLSDA), the proposed PCA-LSTM method applying the same spectral pretreatment achieved substantial improvement in the pixel-wise classification (i.e., accuracy increased from 59.97% of PLSDA to 85.73% of PCA-LSTM). When projecting the pixel-wise model to object-based classification, the PCA-LSTM approach produced an accuracy of 100%, correctly classifying the whole 21 film samples in the independent test set, while PLSDA only led to an accuracy of 80.95%. The proposed method is powerful and versatile in utilizing distinctive characteristics of time dependencies from multivariate time series dataset, which could be adapted to suit non-congruent images over time sequences as well as spectroscopic data.An electrochemical platform based on a screen-printed carbon electrode (SPCE) is developed to detect parathyroid hormone (PTH). A nanocomposite of multi-walled carbon nanotube (MWCNT) and gold nanoparticles (AuNP) was deposited on the SPCE to immobilize antibodies and horseradish peroxidase (HRP). MWCNT improved the stability and conductivity of the immunosensor because of its good electron-transfer ability and tubular structure. The AuNP not only provided a large surface area for antibody immobilization, but it also enhanced the electrochemical signal for enzyme-linked immunosensing. Cyclic voltammetry showed both electron transfer and the effective surface area were increased on the modified electrode. The characteristics of the modified SPCE were assayed by Raman spectroscopy, scanning electron microscopy, atomic force microscopy, and electrochemical techniques. The linear detection range of this PTH immunosensor was within 1-300 pg/ml, and the electrochemical performance was not affected by interference from protein components in human serum. After storage at 4 °C for 28 days, 85% PTH sensing ability of this immunosensor was maintained compared to the freshly prepared one using the SWV and DPV methods. The relative standard deviations of all measurements were within 3-8% for both voltammetric methods. These results indicated the developed immunosensor had good stability and reproducibility. This PTH immunosensor had a detection limit of 0.886 and 0.065 pg/ml for the differential pulse voltammetry and square wave voltammetry, respectively. We provided a quick analysis of serum PTH which might be used as an electrochemical immunosensing platform for point-of-care testing.Fabrication of non-enzymatic electrochemical sensors based on metal oxides with low valence-state for nanomolar detection of H2O2 has been a great challenge. In this work, a novel neuron-network-like Cu-MoO2/C hierarchical structure was simply prepared by in-situ pyrolysis of 3D bimetallic-organic framework [Cu(Mo2O7)L]n [L N-(pyridin-3-ylmethyl)pyridine-2-amine] crystals. Meanwhile, the MoO2/C nano-aggregates were also obtained by liquid phase copper etching. Subsequently, two non-enzymatic electrochemical sensors were fabricated by simple drop-coating of the above two materials on the surface of glassy carbon electrode (GCE). Electrochemical measurements indicate that the Cu-MoO2/C/GCE possesses highly efficient electrocatalytic H2O2 property during wider linear range of 0.24 μM-3.27 mM. At room temperature, the Cu-MoO2/C composite displays higher sensitivity (233.4 μA mM-1 cm-2) and lower limit of detection (LOD = 85 nM), which are 1 and 2.5 times larger than those of MoO2/C material, respectively. Such excellent ability for trace H2O2 detection mainly originates from the synergism of neuron-network-like structure, enhanced electrical conductivity and increased active sites caused by low valence-state MoO2 and co-doping of Cu and carbon, and even the interaction between Cu and Mo. In addition, the H2O2 detection in spiked human serum and commercially real samples indicates that the Cu-MoO2/C/GCE sensor has certain potential application in the fields of environment and biology.Hydrogen deuterium exchange coupled with mass spectrometry (HDX-MS) is a powerful technique for the characterization of protein dynamics and protein interactions. https://www.selleckchem.com/products/dn02.html Recent technological developments in the HDX-MS field, such as sub-zero LC separations, large-scale data analysis tools, and efficient protein digestion methods, have allowed for the application of HDX-MS to the analysis of multi protein systems in addition to pure protein analysis. Still, high-throughput HDX-MS analysis of complex samples is not widespread because the co-elution of peptides combined with increased peak complexity after labeling makes peak de-convolution extremely difficult. Here, for the first time, we evaluated and optimized long gradient subzero-temperature ultra-high-pressure liquid chromatography (UPLC) separation conditions for the HDX-MS analysis of complex protein samples such as E. coli cell lysate digest. Under the optimized conditions, we identified 1419 deuterated peptides from 320 proteins at -10 °C, which is about 3-fold more when compared with a 15-min gradient separation under the same conditions. Interestingly, our results suggested that the peptides eluted late in the gradient are well-protected by peptide-column interactions at -10 °C so that peptides eluted even at the end of the gradient maintain high levels of deuteration. Overall, our study suggests that the optimized, sub-zero, long-gradient UPLC separation is capable of characterizing thousands of peptides in a single HDX-MS analysis with low back-exchange rates. As a result, this technique holds great potential for characterizing complex samples such as cell lysates using HDX-MS.